Enabling knowledge management in the Agronomic Domain

Enabling knowledge management in the Agronomic Domain

Pierre Larmande Ins-tute of Computa-onnal Biology

(IBC) [email protected]

Mul7-‐scale omics integra7on

BIG DATA

Research areas

Workflow management

Seman7c web

Outline

•  Data integration challenges in the Life Sciences"

•  Ontologies/ Semantic Web Technologies"

•  Agronomic Linked Data project"

•  NGS Big Data project"

Data landscape in the Life Sciences

•  The availability of biological data has increased"

•  Advancements in:"•  computational biology"•  genome sequencing"•  high-throughput technologies "

•  Integrative approaches are necessary to understand the functioning of biological systems"

•  Lack of effective approaches to integrate data that has created a gap between data and knowledge"

•  Need for an effective method to bridge gap between data and underlying meaning"

•  Harvest the power of overlaying different data sets"

Data integration challenges

•  Today’s Web content is suitable for human consumption"

•  Collection of documents"•  the existence of links that establish connections

between documents"

•  Low on data interoperability and lacks semantics."

Today’s Web

•  Ontologies are formal representations of knowledge - definitions of concepts, their attributes and relations between them."

•  To integrate data, improve machine interoperability and data analysis required a conceptual scaffold."

•  Ontological terms used across databases"•  provide cross-domain common entry points in the description."

•  An array of ontologies are being used to bring structured integration of various datasets."

•  The Open Biomedical Ontologies (OBO) initiative:"•  serves as an umbrella for well structured orthogonal

ontologies."•  Ontologies represented in OBO format and OWL"

Ontologies

Semantic Web Technology

•  An extension of the current Web technologies."

•  Enables navigation and meaningful use of digital resources."

•  Support aggregation and integration of information from diverse sources."

•  Based on common and standard formats."

Resource Description Framework (RDF)

•  Framework for representing information about resources on the Web"

•  Provides a labeled connection between two resources"

•  Uses Unique Resource Identifiers (URI)"

•  Statements take the form of triples:"

Subject Predicate Object

<Gene_A> <codes_for> <Protein_A>

RDF Triple

•  Combining the triples results in a directed, labeled graph."

<Gene_A>

<Protein_A> <has_func7on>

<BP_A>

<MF_A>

<Gene_X>

<regulates>

SPARQL

•  Language which allows querying RDF models (graphs) "

•  Powerful, flexible"

•  Its syntax is similar to the one of SQL"

Agronomic Linked Data (AgroLD)

•  Semantic web based system that captures knowledge pertaining to plant data"

•  Aim:"

•  Capability to answer complex real life questions"

•  Efficient information integration / retrieval"

•  Easy extensibility"

AgroLD – Phase I

•  AgroLD will be developed in phases – "

•  Phase I: includes data on Oryza sps. and Arabidopsis thaliana!

•  SPARQL endpoint: http://bioportal.lirmm.fr:8081/test/ "

AgroLD

•  Integrates information from:"

•  Ontologies: Gene Ontology, Plant Ontology, Plant Trait Ontology, Plant Environment Ontology, Crop Ontology"

•  Other information sources: "

•  Gene/Protein information: GOA, Gramene, Tair, OryzaBase, UniPort "

•  QTL information: Gramene"

•  Pathway information: RiceCyc, AraCyc"

•  Germplasm information: Oryza Tag Line"

•  Homology prediction: GreenPhyl"

Information in AgroLD

Biological process Cellular

Component

Molecular Function

Interaction

Gene

Taxon

Protein

protein

Modification

Pathway

has_par7cipant

contains

has_func7on

has_agent

acts_on

is_member_of

codes_for

occurs_in

has_source

protein

Target Gene

Knowledge representation in AgroLD

Future directions

•  Phase II: having both wider and deeper coverage to promote comparative analysis"•  Include varied data types – gene expression data, protein-protein

interaction, Transcription factor- target gene "

•  Developing methods to aid the process of hypotheses generation - e.g. inference rules."

•  Pluggable with workflow platforms e.g.: Galaxy, OpenAlea (VirtualPlants)."

•  Engage with biologists to mobilise ‘user-pull’:"•  Develop real world use cases – studying the molecular mechanism

of panicle differentiation in rice"

Gigwa: Genotype Inves7gator for Genome Wide Analyses

Aims of Gigwa

•  Manage genomic, transcriptomic and genotyping data resul-ng from NGS analyses

•  Handle large VCF files to filter, query and extract data

•  Provide a web user interface to make the system accessible for all

Main features

o  Based on NoSQL technology o  Handles VCF files (Variant Call Format) and annota-ons

o  Supports mul-ple variant types: SNPs, InDels, SSRs, SV

o  Powerful genotyping queries o  Easily scalable with MongoDB sharding

o  Transparent access

Demo – Database selec7on

Mul--‐database with restricted access

Demo – datatypes selec7on

Several types of selection: Variant search is restricted to the selected reference sequences and subset of individuals

Demo -‐ Queries

Predefined queries

Demo – Filters selec7on

Several filters

Demo – browsing results

Acknowledgements

Dr. Patrick Valduriez!

Dr. Clement Jonquet

Dr. Manuel Ruiz! Guilhem Sempéré!

Alexis Dereeper Gautier Sarah Dr. Aravind Venkatesan

Florian Philippe Contact: [email protected]

Enabling knowledge management in the Agronomic Domain

Science

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